Generative AI is a form of artificial intelligence that produces new content based on an instruction. You describe what you want and the system generates text, images, code or audio. Tools like ChatGPT, Claude and Gemini are examples of generative AI.
What is the basis of generative AI?
The basis of generative AI is a large language model or similar system trained on huge amounts of data. During training, the model learns to recognise patterns in that data. Based on those patterns, it can generate new content similar to what it has seen during training.
The fundamental principle is probability. Given an instruction, the model calculates what the most logical or appropriate output is. In text generation, the model predicts word for word what follows logically, based on everything it has learned. That is why the output sounds fluent and coherent: the model has learnt which combinations of words are common in which context.
More on how this technical process works in detail explains our article on how AI works from.
How exactly does generative AI work?
When you enter a prompt, the model processes your text through a mechanism called self-attention. Each word in your instruction is weighted against all other words so that the system understands which words are relevant in which context. Based on that analysis, the model generates its response.
The quality of the output depends heavily on the quality of the prompt. A vague instruction produces a generic response. A specific instruction with context, purpose and desired tone produces immediately useful output. This is also why prompt engineering has become a concrete skill for professionals working with generative AI on a daily basis.
Generative AI does not learn during use. The model you use today is trained on data up to a certain date and does not update its knowledge based on your conversations. Every session starts again unless you provide context yourself.
What is the difference between AI and generative AI?
Traditional AI systems analyse, classify or predict based on existing data. A spam filter assesses whether an e-mail is spam. A recommendation system selects products based on previous behaviour. These systems work with fixed patterns and do not produce new content.
Generative AI goes a step further: it produces new content that wasn't there. A text, an image, a piece of code. That is the fundamental difference. Generative AI builds on machine learning but adds the ability to actively create, not just classify.
In practice, both forms are increasingly combined. A system can both classify a document and generate a summary of it. If you want a broader picture of the different forms of AI and how they relate to each other, our article on what artificial intelligence is a good starting point.
What is hallucination in generative AI?
Hallucination is the phenomenon where a generative AI system presents information that is factually incorrect but sounds convincing. The model invents facts, quotes sources that do not exist or presents outdated information as current. This happens not because the system is lying, but because it generates the most plausible output based on probability, not the most correct one.
The problem with hallucinations is that the output looks reliable. An AI system that mentions a non-existent legislation or presents a wrong figure does so with the same confident language as when the information is correct. Verification of critical output therefore always remains necessary.
For professionals, hallucination is the main reason to treat AI output as a first draft, not a final product. Always check facts, figures and references through a reliable source before using them in reports or customer communications. You can read more about the risks of AI and how to weigh them in our article on the risks of AI.
What are the applications of generative AI?
Generative AI is widely applicable for professional tasks. The most common applications are writing and editing texts for e-mails, reports and marketing materials, summarising documents, writing and debugging code, structuring presentations and drafting customer communications.
For professionals without a technical background, the threshold is low: you give an instruction in plain language and the system delivers a first version. You adjust that version based on your expertise and knowledge of the context. The system takes over the repetitive, time-consuming part of the task; the content assessment remains human work.
In the ChatGPT course from LearnLLM learn how to structurally deploy generative AI in your daily work, with prompts that produce consistent and actionable output for your specific tasks.
What is multimodal generative AI?
Multimodal generative AI refers to systems that can process multiple types of input and output simultaneously. A multimodal model can analyse an image and write text about it, or convert a text description to an image. Older systems were limited to one modality, e.g. text only.
Modern tools like GPT-4o and Gemini are multimodal. You can upload a photo and ask what's on it, provide a spreadsheet and ask for a summary, or transcribe an audio clip and have it summarised. That combination of modalities makes generative AI suitable for a wider range of work.
What are the limitations of generative AI?
Besides hallucinations, generative AI has other real-world limitations. Training data has an end date, so the system has no knowledge of recent events unless you provide it yourself. The system does not have access to confidential business information unless you explicitly share it in your prompt.
Privacy is a concern. What you enter into a generative AI system is processed through the provider's servers. For confidential customer or corporate data, additional considerations apply. Many providers offer business subscriptions where data is not used for training, but the default settings of free versions are usually less protective.
Generative AI also incorporates the biases of its training data. If that data underrepresents or misrepresents certain groups, the output may reflect that skew. Being aware of those limitations is part of professional AI use.
What is the future of generative AI?
Generative AI is developing rapidly. Models are becoming more accurate, multimodal capabilities are increasing and integration into existing work tools is deepening. Whereas generative AI is now often a separate tool, in the coming years it will increasingly be built into software that professionals are already using.
An emerging development is that of AI agents: systems that autonomously execute multiple steps based on a goal. This requires skills other than just writing prompts, namely assessing and adjusting autonomous AI actions. For professionals, it is relevant to follow this development, even if you are not currently working with agents.
Do you want to understand how ChatGPT as a generative AI system works and what you can do with it in practice? Our comprehensive article explains that step by step.
